Assembly maps,K-theory, and hyperbolic groups

Following Connes and Moscovici, we show that the Baum-Connes assembly map forK _*(C*v) is rationally injective when is word-hyperbolic, implying the Equivariant Novikov conjecture for such groups. Using this result in topologicalK-theory and Borel-Karoubi regulators, we also show that the corresponding generalized assembly map in algebraicK-theory is rationally injective.     

GPS电子地图坐标转换算法研究

GPS车载导航系统现在已经运用得十分广泛,然而GPS采用的是WGS-84坐标系,目前我国的各类地图均采用54北京坐标或是80西安坐标,两者的差距可达0～120m.要使用各类电子导航装置就需要把WGS-84坐标转换到54北京坐标系下,并和电子地图中的像素点对应起来.本文主要介绍一种坐标转换算法,并且通过实测检验,证实算法效果良好.     

Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.     

A novel video abstraction method based on fast clustering of the regions of interest in key frames

Online video nowadays has become one of the top activities for users and has become easy to access. In the meantime, how to manage such huge amount of video data and retrieve them efficiently has become a big issue. In this article, we propose a novel method for video abstraction based on fast clustering of the regions of interest (ROIs). Firstly, the key-frames in each shot are extracted using the average histogram algorithm. Secondly, the saliency and edge maps are generated from each key-frame. According to these two maps, the key points for the visual attention model can be determined. Meanwhile, in order to expand the regions surrounding the key points, several thresholds are calculated from the corresponding key-frame. Thirdly, based on the key points and thresholds, several regions of interest are expanded and thus the main content in each frame is obtained. Finally, the fast clustering method is performed on the key frames by utilizing their ROIs. The performance and effectiveness of the proposed video abstraction algorithm is demonstrated by several experimental results.     

A novel multi-group exploiting modification direction method based on switch map

In this paper, we proposed a novel adjustable data hiding method. Our proposed method, which is based on exploiting modification direction (EMD) method, is called the “multi-group exploiting modification direction” method. In the multi-group method, we combine several pixel-groups to embed secret data according to the constructed switch map to simply avoid the conversion redundancy of the EMD method and the spatial redundancy of the section-wise strategy, as well as to efficiently increase the probability of achieving larger embedding capacity. According to the experimental results and mathematical justification, we verified that the multi-group method can achieve higher embedding payload and better visual quality of the image than the EMD method and section-wise strategy for simulated and real secret data.     

Thermomechanical formulation of ductile damage coupled to nonlinear isotropic hardening and multiplicative viscoplasticity

In this paper, we present a thermomechanical framework which makes use of the internal variable theory of thermodynamics for damage-coupled finite viscoplasticity with nonlinear isotropic hardening. Damage evolution, being an irreversible process, generates heat. In addition to its direct effect on material's strength and stiffness, it causes deterioration of the heat conduction. The formulation, following the footsteps of Simó and Miehe (1992), introduces inelastic entropy as an additional state variable. Given a temperature dependent damage dissipation potential, we show that the evolution of inelastic entropy assumes a split form relating to plastic and damage parts, respectively. The solution of the thermomechanical problem is based on the so-called isothermal split. This allows the use of the model in 2D and 3D example problems involving geometrical imperfection triggered necking in an axisymmetric bar and thermally triggered necking of a 3D rectangular bar.     

Newton trajectories for the tilted Frenkel–Kontorova model

ABSTRACT

Newton trajectories are used for the Frenkel–Kontorova model of a finite chain with free-end boundary conditions. We optimise stationary structures, as well as barrier breakdown points for a critical tilting force were depinning of the chain happens. These special points can be obtained straight forwardly by the tool of Newton trajectories. We explain the theory and add examples for a finite-length chain of a fixed number of 2,?3,?4,?5 and 23 particles.     

Some extensions of the operator entropy type inequalities

In this paper, we define the generalised relative operator entropy and investigate some of its properties such as subadditivity and homogeneity. As application of our result, we obtain the information inequality. In continuation, we establish some reverses of the operator entropy inequalities under certain conditions by using the Mond–Pe?ari? method.     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.